Scaling sampling-based motion planning to humanoid robots

Planning balanced and collision-free motion for humanoid robots is non-trivial, especially when they are operated in complex environments, such as reaching targets behind obstacles or through narrow passages. Research has been done in particular to plan such complex motion on humanoids, however, these approaches are typically restricted to particular robot platforms and environments, which can not be easily replicated nor applied. We propose a method that allows us to apply existing sampling-based algorithms directly to plan trajectories for humanoids by utilizing a customized state space representation, biased sampling strategies, and a steering function based on a robust inverse kinematics solver. Our approach requires no prior offline computation, thus one can easily transfer the work to new robot platforms. We tested the proposed method by solving practical reaching tasks on a 38 degrees-of-freedom humanoid robot, NASA Valkyrie, showing that our method is able to generate valid motion plans that can be executed on advanced full-size humanoid robots.